

import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:      Tuple   		=			logging.get_logger(__name__)

__snake_case				:      List[str]   		=			{
    """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
    """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}



class        A__(a_							):
						"""simple docstring"""






						_A    :				Tuple       					=			'''xlnet'''
						_A    :				Optional[int]       					=			['''mems''']
						_A    :				str       					=			{
						    '''n_token''': '''vocab_size''',  # Backward compatibility
						    '''hidden_size''': '''d_model''',
						    '''num_attention_heads''': '''n_head''',
						    '''num_hidden_layers''': '''n_layer''',
						}





						def __init__(    self  ,      _lowercase=32_000  ,      _lowercase=1_024  ,      _lowercase=24  ,      _lowercase=16  ,      _lowercase=4_096  ,      _lowercase="gelu"  ,      _lowercase=True  ,      _lowercase="bi"  ,      _lowercase=0.0_2  ,      _lowercase=1e-12  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=None  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase=False  ,      _lowercase=-1  ,      _lowercase=False  ,      _lowercase="last"  ,      _lowercase=True  ,      _lowercase="tanh"  ,      _lowercase=0.1  ,      _lowercase=5  ,      _lowercase=5  ,      _lowercase=5  ,      _lowercase=1  ,      _lowercase=2  ,      **_lowercase  ,      )     -> Any:
									a_						:						Union[str, Any] 							= vocab_size
									a_						:						Any 							= d_model
									a_						:						List[Any] 							= n_layer
									a_						:						str 							= n_head
									if d_model % n_head != 0:
												raise ValueError(F'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''    )
									if "d_head" in kwargs:
												if kwargs["d_head"] != d_model // n_head:
															raise ValueError(
															    F'''`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})'''    )
									a_						:						List[Any] 							= d_model // n_head
									a_						:						int 							= ff_activation
									a_						:						List[Any] 							= d_inner
									a_						:						List[str] 							= untie_r
									a_						:						List[str] 							= attn_type

									a_						:						int 							= initializer_range
									a_						:						List[Any] 							= layer_norm_eps

									a_						:						Optional[Any] 							= dropout
									a_						:						List[Any] 							= mem_len
									a_						:						Any 							= reuse_len
									a_						:						int 							= bi_data
									a_						:						List[str] 							= clamp_len
									a_						:						Tuple 							= same_length

									a_						:						Dict 							= summary_type
									a_						:						List[Any] 							= summary_use_proj
									a_						:						Union[str, Any] 							= summary_activation
									a_						:						int 							= summary_last_dropout
									a_						:						List[str] 							= start_n_top
									a_						:						str 							= end_n_top

									a_						:						Dict 							= bos_token_id
									a_						:						str 							= pad_token_id
									a_						:						Union[str, Any] 							= eos_token_id

									if "use_cache" in kwargs:
												warnings.warn(
												    """The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"""
												    """ instead."""  ,      _lowercase  ,      )
												a_						:						Dict 							= kwargs["""use_cache"""]

									a_						:						List[Any] 							= use_mems_eval
									a_						:						str 							= use_mems_train
									super().__init__(pad_token_id=_lowercase  ,      bos_token_id=_lowercase  ,      eos_token_id=_lowercase  ,      **_lowercase    )





						@property
						def 			UpperCamelCase__					(    self    )     -> Tuple:
									logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''    )
									return -1





						@max_position_embeddings.setter
						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Optional[int]:
									# Message copied from Transformer-XL documentation
									raise NotImplementedError(
									    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''    )





from __future__ import annotations


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if len(a__) == 0:
						return []
			a_							,					a_						:						List[Any] 							= min(a__), max(a__)
			a_						:						Tuple 							= int(max_value - min_value) + 1
			a_						:						list[list] 							= [[] for _ in range(a__)]

			for i in my_list:
						buckets[int(i - min_value)].append(a__)

			return [v for bucket in buckets for v in sorted(a__)]


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]


